DataStax acquires machine learning company Kaskada to unlock real-time AI
DataStax, the real-time AI company, announced that it has acquired Kaskada, a machine learning (ML) company that first solved the management, storage and access of time-based data to train behavioral ML models and deliver the immediate, actionable insights that fuel artificial intelligence (AI). Both DataStax and Kaskada have a track record of contributing to the open source community. Datastax will initially open source the core technology of Kaskada, and plans to offer a new machine learning cloud service later this year.
Most machine learning initiatives fail to deliver the results businesses need because the process is manual, complex and frustrating. This problem compounds the underperformance of many models because they lack the relevance and context of real-time data. The addition of Kaskada to DataStax’s portfolio of cloud services – which today includes the massively scalable Astra DB database-as-a-service built on Apache Cassandra® and event streaming with Astra Streaming – will provide organizations with a single environment to easily and cost-effectively deliver applications infused with real-time AI, using an advanced ML/AI model proven by industry leaders like Netflix and Uber.
“Businesses must operate in real time, use data to drive operations and fuel immediate, informed decisions and actions,” said Chet Kapoor, DataStax Chairman and CEO. “DataStax has many customers who already use real-time data, and with Kaskada as part of our service portfolio, we can empower them to use that data to create powerful experiences for their customers with real-time AI. It’s an exciting time for DataStax, and we have a clear new mandate: real-time AI for everyone.”
“Many companies struggle to see success with their big data projects because they don’t have the luxury of large ML and data engineering organizations—the costs are high and the time to impact is long,” said Davor Bonaci, Kaskada CEO. “We are thrilled to team up with DataStax to enable the real-time AI stack that just works, powered by data from Astra DB.”
AI at scale: Game-changing potential, but difficult to achieve
According to Gartner, “By 2027, over 90% of new software applications developed in the enterprise will contain ML models or services as enterprises use the vast amounts of data available to the enterprise. These models will add data-driven intelligence to applications by integrating models that delivers second-best actions, forecasting, scoring, risk assessment and many other attributes for both customer and employee transactions.1”
Yet many organizations have found it challenging to integrate this intelligence into their operational applications.
Matt Aslett, vice president and director of research at Ventana Research noted, “The rise of intelligent applications with personalization and artificial intelligence is impacting the requirements for operational data platforms to support real-time analytics functionality. The need for real-time interactivity means that these applications cannot be served by traditional processes that rely on batch extraction, transformation and loading of data from operational data platforms to analytical data platforms for analysis. Instead, they rely on the analysis of data in the operational data platform to accelerate decision-making or improve the customer experience. High costs, complexity and scaling issues have been roadblocks for many organizations when it comes to achieving dynamic real-time intelligence in their operational platforms.”
Caskada technology is designed to process massive amounts of event data streaming or stored in databases, and its unique time-based capabilities create and update features for ML models based on event sequences, or over time. It enables customers to adapt to rapidly evolving content and creates functions asynchronously, allowing applications to make millions of predictions based on unique contexts.
“In e-commerce, you need to be able to immediately act on insights to give customers the most impactful experiences; and that requires applying machine learning to real-time transactions,” Martin Brodbeck, CTO at Priceline. “We have millions of customers using our website and mobile apps at any given time, and Astra DB is a powerful component of the Priceline data infrastructure. Our machine learning algorithms use massive amounts of data to provide valuable customer insights, greater personalization and better travel recommendations, fueling our larger customer ecosystem.”
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